25 July 2025
Knowledge graphs: The next frontier in AI-powered business intelligence
It will come as no surprise that artificial intelligence, and generative AI specifically, is continuing to have a profound impact on how businesses process and leverage their information. That said, critical limitations in how traditional AI systems understand context and relationships across complex data are being evident. While the pairing of LLMs and retrieval-augmented generation (RAG) has marked a significant step forward, this too has its limits. GraphRAG, powered by knowledge graphs, presents a quantum leap in AI capability that businesses on the cutting edge cannot afford to ignore.
Traditional RAG systems retrieve information through vector similarity searches, essentially matching text fragments based on semantic similarity. While functional, this approach suffers from what our Head of AI Development, Ken Chang, identifies as “fragmented context awareness.” The system treats each piece of information as an isolated fragment, missing the crucial connections that enable sophisticated reasoning and comprehensive understanding.
GraphRAG transforms this paradigm by introducing knowledge graphs, sophisticated data structures that map entities, relationships, and connections in a highly organized, relational framework. If that sounds a little overly complex, think of it as something akin to a minutely detailed and extensive mind map, in which all relationships are also labelled. Instead of retrieving disconnected text chunks, GraphRAG navigates through interconnected data, enabling multi-hop reasoning and extracting deeper insights from complex information networks.
As the adage goes: seeing is believing. In order to demonstrate the massive leap in reasoning quality available through migration from RAG to GraphRAG, Ken set up a little experiment.
Tests began by randomly selecting an article about fibre-optic communication technology. The experiment involved processing the same source material through two different methodologies:
| Traditional RAG processing | GraphRAG processing |
|---|---|
| Text was chunked into segments | Text was chunked and embedded |
| Chunks were embedded using vector representations | All entities and relationships were extracted from the content |
| Content was stored in a vector database for similarity-based retrieval | A comprehensive knowledge graph was constructed with nodes representing entities and edges representing relationships |
| The graph created a logical structure connecting related concepts |
The resulting knowledge graph visualization revealed the interconnected nature of fibre-optic technology concepts, showing relationships between technical specifications, applications, advantages, and implementation considerations that traditional RAG processing completely missed.
Once we had both traditional RAG and GraphRAG systems established, it came time to test the impact of those different processing methods. We asked a simple question to both: “Why are fibre-optics a good technology for broadband?” Let’s take a look at the responses.
| Traditional RAG’s response | GraphRAG’s response |
|---|---|
| 1. High Bandwidth and Distance Capability: Researchers have achieved a “record bandwidth distance product of over 100 petabit kilometres per second” using fibre-optic communication, indicating its ability to transmit vast amounts of data over significant distances. 2. Advantages over Electrical Transmission: It offers “advantages over electrical transmission” and has “largely replaced copper wire communications in backbone networks in the developed world,” suggesting superior performance for high-capacity applications like broadband. 3. Long-Distance Transmission with Low Attenuation: Optical fibre was developed with “attenuation low enough for communication purposes” and is suitable for “transmitting light through fibre optic cables for long distances,” which is crucial for building extensive and reliable broadband networks. | Fibre Optics: A Reliable and Efficient Technology for Broadband Fibre optics has emerged as a premier technology for broadband due to its unparalleled capabilities in terms of speed, reliability, and security. Here’s a summary of the key benefits: High-Speed Data Transmission Fibre optic cables can support speeds of up to 10 Gbps or more [Data: Reports (1)], making them an ideal solution for demanding applications like video streaming, online gaming, and cloud computing. Reliable and Efficient Data Transfer Fibre optics provides a stable and secure connection, resistant to electromagnetic interference (EMI) and radio-frequency interference (RFI) [Data: Reports (2)]. This ensures that data is transmitted efficiently over long distances, minimizing errors and latency. Scalability and Flexibility Fibre optic cables can be easily upgraded or reconfigured as demand increases, making them a scalable solution for growing networks. Additionally, fibre optics can support multiple services simultaneously, such as voice, video, and data [Data: Reports (3)]. Security and Reliability Fibre optic cables are less prone to physical damage and can withstand harsh environmental conditions, such as extreme temperatures and humidity [Data: Reports (4)]. This reduces the need for frequent maintenance and repairs, ensuring a reliable connection. Future-Proofing As technology continues to evolve, fibre optics is well-positioned to support emerging applications like 5G wireless networks, IoT devices, and cloud computing. Its high-speed capabilities and scalability make it an ideal foundation for future-proofing broadband infrastructure. In summary, fibre optics offers a reliable, efficient, and secure solution for broadband applications, making it an excellent choice for supporting the growing demands of modern communication networks. |
Even from a simple experiment such as this, we can clearly see several critical advantages from the response using GraphRAG.
Firstly, we see a much more comprehensive, contextual understanding of the topic at hand. GraphRAG understood that the question about broadband technology could be answered by more than just technical specifications, but also in alignment with business and strategic considerations. It was then able to maps technical properties such as interference resistance to strategic values such as increased reliability and reduced maintenance costs.
There were also differences in how GraphRAG organized information when compared to traditional RAG: rather than a list of isolated facts, it was able to logically organize information to answer different aspects of the original question.
So now we’ve seen first-hand how knowledge graphs can provide AI agents with a much deeper and more complex understanding of a given topic. What does this mean for your business?
By moving beyond traditional RAG and employing knowledge graphs, your AI systems begin to understand how individual facts relate to each other, providing a “big picture” approach that is lacking from traditional RAG. This additional context allows your AI systems to provide more comprehensive and accurate insights for strategic decision-making.
Take the example of a pharmaceutical company. Rather than analyzing drug interactions based on specific individual studies, a knowledge graph would allow them to understand the complex relationships between compounds, patient demographics, side effects, and treatment outcomes across their entire knowledge base.
The modern business landscape, often involving entities across regional markets, faces increasingly complex questions that require information from multiple sources and domains. While traditional RAG systems can provide simple responses, they often fail when queries demand multi-step logic or reasoning over relationships.
If a firm specializing in financial services were to investigate which of their clients in the renewable energy sector have exposure to supply chain risks in Southeast Asia, and how that might impact their ESG ratings, for example, GraphRAG could traverse multiple relationship layers to provide comprehensive answers.
The potential for hallucinations is potentially one of the most commonly discussed risks of working with AI. Many of us have seen time and time again how general models are able to produce plausible but incorrect information at scale. The structured nature of knowledge graphs significantly reduces this risk by grounding AI responses in explicit, verified relationships.
Many businesses now operate across linguistic and cultural boundaries (we should know – we’ve been in the localization industry for almost 40 years!) Knowledge graphs excel in multilingual environments because they focus on relationships and entities rather than language-specific text matching.
The key to creating multilingual knowledge graphs is in the dissection of meaning from language. This can be done through the application of universal entity IDs, which can then be mapped to canonical names across each language. In one such example you might see ‘Entity 001’ existing as the universal entity ID, which is then mapped to both ‘Alpha CRC’ in English, and ‘アルファCRC’ in Japanese.
Of course, this graph is then dependent on the quality of translation used across the original data. We’d therefore recommend working with professional localization service providers in order to audit whether your translations are accurate, and whether your cultural statements are correct across markets.
Of course, creating knowledge graphs isn’t quite as simple as clicking your fingers – especially if you want data that is effective and accurate.
It’s the same for knowledge graphs as it is for traditional localization assets such as translation memories: the output is only going to be as good as the data you provide. Businesses must invest in data governance and integration processes to maximize the value of their knowledge graph implementations.
Effective knowledge graphs require deep understanding of business domains and relationships. The most successful implementations combine AI capabilities with human domain expertise, especially when looking to incorporate multilingual faculties.
Knowledge graphs can become complex quickly. Businesses should plan for scalability from the outset, considering both technical infrastructure and governance processes.
As AI systems become more sophisticated, the ability to understand complex relationships will become a key differentiator. Businesses that invest in knowledge graph capabilities today will be better positioned to accelerate innovation, improve decision-making, and reduce risk across their future development.
The evolution from RAG to GraphRAG represents more than a technical upgrade; it’s a fundamental shift in how AI systems understand and process information. Examples such as our fibre-optic communication experiment help to demonstrate that statements such as this aren’t purely theoretical either – there are clear, practical differences in output quality.
Businesses that recognize and prepare for this shift will gain significant competitive advantages in accuracy, efficiency, and insight generation. The difference between receiving a list of disconnected facts and getting a comprehensive, structured analysis that directly addresses business needs cannot be overstated.
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